System Design Principles Every Engineer Should Know

As Goh Ling Yong, I've spent years working on complex systems and have learned that solid design principles are the foundation of great software. In this comprehensive guide, I'll share the essential principles every engineer should master.

Table of Contents

1. Introduction
2. Scalability Principles
3. Reliability and Fault Tolerance
4. Performance Optimization
5. Real-World Examples
6. Best Practices
7. Common Mistakes to Avoid
8. Conclusion

Introduction

System design is more than just writing code—it's about creating architectures that can scale, adapt, and evolve with business needs. Whether you're building a startup MVP or an enterprise system, these principles will guide your decisions.

Scalability Principles

Horizontal vs Vertical Scaling

Horizontal scaling (scaling out) involves adding more machines to your pool of resources, while vertical scaling (scaling up) means adding more power to an existing machine. Each approach has its place:

Horizontal Scaling Benefits:

• Better fault tolerance
• More flexible growth
• Cost-effective at large scale
• No single point of failure

Vertical Scaling Benefits:

• Simpler to implement initially
• No code changes required
• Better for monolithic applications
• Lower network overhead

Load Balancing

Distributing traffic across multiple servers is crucial for:

• Preventing server overload
• Improving response times
• Ensuring high availability
• Enabling zero-downtime deployments

Popular load balancing algorithms include:

• Round Robin
• Least Connections
• IP Hash
• Weighted Round Robin

Reliability and Fault Tolerance

The CAP Theorem

In distributed systems, you can only guarantee two of three properties:

• Consistency: All nodes see the same data
• Availability: Every request receives a response
• Partition Tolerance: System continues despite network failures

Understanding CAP helps make informed trade-offs.

Circuit Breaker Pattern

Prevent cascading failures by:

1. Detecting failures quickly
2. Stopping requests to failing services
3. Allowing time for recovery
4. Gradually resuming traffic

Performance Optimization

Caching Strategies

Strategic caching can dramatically improve performance:

Cache-Aside Pattern:

1. Check cache
2. If miss, query database
3. Store in cache
4. Return result

Write-Through Cache:

• Write to cache and database simultaneously
• Ensures consistency
• Slightly slower writes

Write-Behind Cache:

• Write to cache immediately
• Async write to database
• Faster writes, eventual consistency

Database Optimization

Key strategies:

• Indexing: Speed up queries
• Denormalization: Reduce joins for read-heavy workloads
• Partitioning: Split large tables
• Connection pooling: Reuse database connections

Real-World Examples

E-commerce Platform

For an e-commerce system, I implemented:

• CDN for static assets
• Redis caching for product catalog
• Message queues for order processing
• Read replicas for reporting

This architecture handled 10x traffic during sales with zero downtime.

Analytics Dashboard

For a real-time analytics platform:

• Stream processing with Kafka
• Time-series database for metrics
• Materialized views for aggregations
• WebSocket for live updates

Response times stayed under 100ms even with millions of events per minute.

Best Practices

1. Start Simple: Don't over-engineer early
2. Monitor Everything: You can't improve what you don't measure
3. Automate: CI/CD, testing, deployments
4. Document: Architecture decisions, trade-offs
5. Plan for Failure: Everything will fail eventually
6. Security First: Build it in, not bolt it on

Common Mistakes to Avoid

Premature Optimization

Don't optimize for scale you don't have. Start simple, measure, then optimize based on real data.

Ignoring Network Latency

Network calls are expensive. Design with this in mind:

• Batch requests
• Use caching
• Consider data locality

Single Points of Failure

Always ask: "What happens if this fails?" Then eliminate or mitigate those risks.

Not Considering Operational Costs

Architecture decisions have ongoing costs:

• Infrastructure
• Maintenance
• Monitoring
• On-call burden

Conclusion

System design is a journey, not a destination. These principles provide a solid foundation, but real expertise comes from experience—both successes and failures.

Remember: the best design is one that solves your actual problems, not the most impressive one on paper.

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About the Author: Goh Ling Yong is an engineer and business analyst with over 10 years of experience building scalable systems. He specializes in system architecture, distributed systems, and helping organizations bridge the gap between technical implementation and business outcomes. Learn more about Goh Ling Yong.